[legacy] clean up legacy code (#4743)

* [legacy] remove outdated codes of pipeline (#4692)

* [legacy] remove cli of benchmark and update optim (#4690)

* [legacy] remove cli of benchmark and update optim

* [doc] fix cli doc test

* [legacy] fix engine clip grad norm

* [legacy] remove outdated colo tensor (#4694)

* [legacy] remove outdated colo tensor

* [test] fix test import

* [legacy] move outdated zero to legacy (#4696)

* [legacy] clean up utils (#4700)

* [legacy] clean up utils

* [example] update examples

* [legacy] clean up amp

* [legacy] fix amp module

* [legacy] clean up gpc (#4742)

* [legacy] clean up context

* [legacy] clean core, constants and global vars

* [legacy] refactor initialize

* [example] fix examples ci

* [example] fix examples ci

* [legacy] fix tests

* [example] fix gpt example

* [example] fix examples ci

* [devops] fix ci installation

* [example] fix examples ci

colossalai/legacy/zero/__init__.py

@@ -0,0 +1,45 @@
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+
+from colossalai.logging import get_dist_logger
+
+from .init_ctx import ZeroInitContext, no_shard_zero_context, no_shard_zero_decrator
+from .shard_utils import BucketTensorShardStrategy, TensorShardStrategy
+from .sharded_model import ShardedModelV2
+from .sharded_optim import ShardedOptimizerV2
+
+
+def convert_to_zero_v2(model: nn.Module, optimizer: torch.optim.Optimizer, model_config,
+                       optimizer_config) -> Tuple[ShardedModelV2, ShardedOptimizerV2]:
+    """
+    A helper function to integrate the model and optimizer with ZeRO optimizer and off-loading
+
+    :param model: Your model object
+    :type model: :class:`torch.nn.Module`
+    :param optimizer_config: Your optimizer object
+    :type optimizer_config: :class:`dict`
+
+    :return: (model, optimizer)
+    :rtype: Tuple
+    """
+
+    logger = get_dist_logger('convert_to_zero_v2')
+
+    logger.info(f'optimizer_config is {optimizer_config}', ranks=[0])
+    if optimizer_config is None:
+        optimizer_config = dict()
+    logger.info(f'model_config is {model_config}', ranks=[0])
+    if model_config is None:
+        model_config = dict()
+
+    zero_model = ShardedModelV2(model, **model_config)
+    zero_optimizer = ShardedOptimizerV2(zero_model, optimizer, **optimizer_config)
+    return zero_model, zero_optimizer
+
+
+__all__ = [
+    'convert_to_zero_v2', 'ShardedModelV2', 'ShardedOptimizerV2', 'ZeroInitContext', 'no_shard_zero_context',
+    'no_shard_zero_decrator', 'TensorShardStrategy', 'BucketTensorShardStrategy'
+]